Identification of potential biomarkers for 2022 Mpox virus infection: a transcriptomic network analysis and machine learning approach
Abstract Monkeypox virus (MPXV), a zoonotic pathogen, re-emerged in 2022 with the Clade IIb variant, raising global health concerns due to its unprecedented spread in non-endemic regions. Recent studies have shown that Clade IIb (2022 MPXV) is marked by unique genomic mutations and epidemiological b...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-80519-7 |
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| author | Joy Prokash Debnath Kabir Hossen Sabrina Bintay Sayed Md. Sayeam Khandaker Preonath Chondrow Dev Saifuddin Sarker Tanvir Hossain |
| author_facet | Joy Prokash Debnath Kabir Hossen Sabrina Bintay Sayed Md. Sayeam Khandaker Preonath Chondrow Dev Saifuddin Sarker Tanvir Hossain |
| author_sort | Joy Prokash Debnath |
| collection | DOAJ |
| description | Abstract Monkeypox virus (MPXV), a zoonotic pathogen, re-emerged in 2022 with the Clade IIb variant, raising global health concerns due to its unprecedented spread in non-endemic regions. Recent studies have shown that Clade IIb (2022 MPXV) is marked by unique genomic mutations and epidemiological behaviors, suggesting variations in host-virus interactions. This study aimed to identify the differentially expressed genes (DEGs) induced by the 2022 MPXV infection through comprehensive bioinformatics analyses of microarray and RNA-Seq datasets from post-infected cell types with different MPXV clades. Subsequently, gene expression network analyses pinpoint the key DEGs, followed by their candidate drug assessment using the Drug SIGnatures DataBase (DSigDB) and validation by multiple machine learning algorithms. Comparative differential gene expression (DGE) analysis revealed 798 DEGs exclusive to the 2022 MPXV invasion in the skin cell types (keratinocytes). Intriguingly, 13 key DEGs were identified across hubs and clusters, highlighting their aberrant expressions in cell cycle regulation, immune responses, and cancer pathways. Biomarker screening via Random Forest (RF) model (selected with PyCaret from multiple models) and validation through t-distributed stochastic neighbor embedding (t-SNE) algorithm, principal component analysis (PCA), and ROC curve analysis employing Logistic Regression and Random Forest, identified 6 key DEGs (TXNRD1, CCNB1, BUB1, CDC20, BUB1B, and CCNA2) as promising biomarkers (AUC > 0.7) for clade IIb infection. This study anticipates that further investigation and clinical trials will catalyze novel detection and therapeutic options to combat 2022 MPXV infection in humans. |
| format | Article |
| id | doaj-art-c9b0afaaad9546d7920d2c34712bf0c7 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c9b0afaaad9546d7920d2c34712bf0c72025-08-20T03:01:55ZengNature PortfolioScientific Reports2045-23222025-01-0115111510.1038/s41598-024-80519-7Identification of potential biomarkers for 2022 Mpox virus infection: a transcriptomic network analysis and machine learning approachJoy Prokash Debnath0Kabir Hossen1Sabrina Bintay Sayed2Md. Sayeam Khandaker3Preonath Chondrow Dev4Saifuddin Sarker5Tanvir Hossain6Department of Biochemistry and Molecular Biology, Shahjalal University of Science and TechnologyDepartment of Biochemistry and Molecular Biology, Shahjalal University of Science and TechnologyDepartment of Biochemistry and Molecular Biology, Shahjalal University of Science and TechnologyDepartment of Biochemistry and Molecular Biology, Shahjalal University of Science and TechnologyChild Health Research FoundationClinical Laboratory, Medi Check Medical Service LimitedDepartment of Biochemistry and Molecular Biology, Shahjalal University of Science and TechnologyAbstract Monkeypox virus (MPXV), a zoonotic pathogen, re-emerged in 2022 with the Clade IIb variant, raising global health concerns due to its unprecedented spread in non-endemic regions. Recent studies have shown that Clade IIb (2022 MPXV) is marked by unique genomic mutations and epidemiological behaviors, suggesting variations in host-virus interactions. This study aimed to identify the differentially expressed genes (DEGs) induced by the 2022 MPXV infection through comprehensive bioinformatics analyses of microarray and RNA-Seq datasets from post-infected cell types with different MPXV clades. Subsequently, gene expression network analyses pinpoint the key DEGs, followed by their candidate drug assessment using the Drug SIGnatures DataBase (DSigDB) and validation by multiple machine learning algorithms. Comparative differential gene expression (DGE) analysis revealed 798 DEGs exclusive to the 2022 MPXV invasion in the skin cell types (keratinocytes). Intriguingly, 13 key DEGs were identified across hubs and clusters, highlighting their aberrant expressions in cell cycle regulation, immune responses, and cancer pathways. Biomarker screening via Random Forest (RF) model (selected with PyCaret from multiple models) and validation through t-distributed stochastic neighbor embedding (t-SNE) algorithm, principal component analysis (PCA), and ROC curve analysis employing Logistic Regression and Random Forest, identified 6 key DEGs (TXNRD1, CCNB1, BUB1, CDC20, BUB1B, and CCNA2) as promising biomarkers (AUC > 0.7) for clade IIb infection. This study anticipates that further investigation and clinical trials will catalyze novel detection and therapeutic options to combat 2022 MPXV infection in humans.https://doi.org/10.1038/s41598-024-80519-7Mpox (monkeypox)2022 MPXV (Clade IIb)DEGsMachine learning (ML) modelsBiomarkerCandidate drugs |
| spellingShingle | Joy Prokash Debnath Kabir Hossen Sabrina Bintay Sayed Md. Sayeam Khandaker Preonath Chondrow Dev Saifuddin Sarker Tanvir Hossain Identification of potential biomarkers for 2022 Mpox virus infection: a transcriptomic network analysis and machine learning approach Scientific Reports Mpox (monkeypox) 2022 MPXV (Clade IIb) DEGs Machine learning (ML) models Biomarker Candidate drugs |
| title | Identification of potential biomarkers for 2022 Mpox virus infection: a transcriptomic network analysis and machine learning approach |
| title_full | Identification of potential biomarkers for 2022 Mpox virus infection: a transcriptomic network analysis and machine learning approach |
| title_fullStr | Identification of potential biomarkers for 2022 Mpox virus infection: a transcriptomic network analysis and machine learning approach |
| title_full_unstemmed | Identification of potential biomarkers for 2022 Mpox virus infection: a transcriptomic network analysis and machine learning approach |
| title_short | Identification of potential biomarkers for 2022 Mpox virus infection: a transcriptomic network analysis and machine learning approach |
| title_sort | identification of potential biomarkers for 2022 mpox virus infection a transcriptomic network analysis and machine learning approach |
| topic | Mpox (monkeypox) 2022 MPXV (Clade IIb) DEGs Machine learning (ML) models Biomarker Candidate drugs |
| url | https://doi.org/10.1038/s41598-024-80519-7 |
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